Quantization test result

Currently mmdeploy support ncnn quantization

Quantize with ncnn

mmpretrain

model	dataset	fp32 top-1 (%)	int8 top-1 (%)
ResNet-18	Cifar10	94.82	94.83
ResNeXt-32x4d-50	ImageNet-1k	77.90	78.20*
MobileNet V2	ImageNet-1k	71.86	71.43*
HRNet-W18*	ImageNet-1k	76.75	76.25*

Note:

• Because of the large amount of imagenet-1k data and ncnn has not released Vulkan int8 version, only part of the test set (4000/50000) is used.

• The accuracy will vary after quantization, and it is normal for the classification model to increase by less than 1%.

OCR detection

model	dataset	fp32 hmean	int8 hmean
PANet	ICDAR2015	0.795	0.792 @thr=0.9
TextSnake	CTW1500	0.817	0.818

Note: mmocr Uses ‘shapely’ to compute IoU, which results in a slight difference in accuracy

Pose detection

model	dataset	fp32 AP	int8 AP
Hourglass	COCO2017	0.717	0.713

Note: MMPose models are tested with flip_test explicitly set to False in model configs.